AWS Bedrock → QuickSilver Pro

The steps

1. 1

   Get a QuickSilver Pro API key

   Sign up at quicksilverpro.io/dashboard. First top-up bonus: we match your first top-up 100%, up to $50 — added to your balance when you top up again (any amount from $5).

2. 2

   Replace the AWS SDK with the OpenAI SDK

   Bedrock requires the AWS SDK (boto3 / aws-sdk) and SigV4 request signing. QuickSilver Pro serves the OpenAI chat-completions shape directly, so you drop that plumbing entirely and use the official openai client with a Bearer API key.

   Bedrock's Converse API maps cleanly to OpenAI chat completions — streaming, tool calling, and structured output behavior carry over. See the full before/after below.

3. 3

   Rename model IDs

   Bedrock uses region-qualified inference-profile IDs. QuickSilver Pro uses short model names. Bedrock does not carry the DeepSeek V4 wave, Qwen 3.6, or Kimi K2.6 — those become newly available on QuickSilver Pro; DeepSeek V4 Pro is the closest reasoning replacement for Bedrock's DeepSeek offering.

   AWS Bedrock	QuickSilver Pro
   Current Bedrock DeepSeek model	deepseek-v4-pro
   (not on Bedrock)	deepseek-v4-flash

4. 4

   Test your core flows end-to-end

   Run one representative request for each feature you use — chat, streaming, tool / function calling, and json_schema strict mode. Apart from the known difference below, any behavioral difference is a bug — report it.

   One known difference: json_schema strict mode is model-dependent. The Claude models do not support it, so a schema there is advisory and the enforced path is a tool with strict: true — structured output.

Full before/after

import boto3, json

client = boto3.client("bedrock-runtime", region_name="us-east-1")

resp = client.converse(
    modelId=os.environ["BEDROCK_DEEPSEEK_MODEL_ID"],
    messages=[{"role": "user", "content": [{"text": "Hi"}]}],
)
text = resp["output"]["message"]["content"][0]["text"]

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.quicksilverpro.io/v1",
    api_key=os.environ["QSP_KEY"],
)

r = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Hi"}],
)
text = r.choices[0].message.content

What you'll pay after switching

Per 1M tokens, input / output. QuickSilver Pro rates vs AWS Bedrock's published per-token pricing.

Common migration pitfalls

Migrating from AWS Bedrock — FAQ

Other migration guides

Need help?

Email [email protected] — a human replies usually within 4 hours. For the broader analysis, see QuickSilver Pro vs AWS Bedrock.